Optimized illumination for imaging

ABSTRACT

Various embodiments are directed to imaging systems and methods for generating an image of a sub-surface feature of an object through a surface of the object. An illumination array may comprise a plurality of illumination sources positioned around the sub-surface feature of the object. An imaging device may comprise an objective. A computer system may be in communication with the illumination array. The computer system may be programmed to calculate an optimized illumination pattern of the plurality of illumination sources for imaging the sub-surface feature; activate the optimized illumination pattern; and instruct the imaging device to capture an image of the sub-surface feature with the imaging device based on reflected illumination from the optimized illumination pattern.

BACKGROUND

In semiconductor fabrication and other fields, it is often necessary ordesirable to image subsurface objects. For example, when a semiconductorchip is constructed according to “flip-chip” mounting techniques,component structures on the chip are obscured by the substrate. Varioussemiconductor fabrication and testing techniques require high-contrastimaging of components. Some examples of these techniques include LaserAssisted Chemical Etching, Focused Ion Beam, and others. Imaging throughcommon substrate materials, such as silicon, is possible, although,difficulties exist.

One method of imaging through substrate material is conventional brightfield microscopy. According to bright field microscopy, illumination isprovided in a direction normal to the substrate surface. An image iscaptured with a camera or other imaging device also oriented normal tothe substrate surface. While this technique can be relativelyinexpensive, the resolution of the resulting images is oftendisappointing. This is, at least in part, because backscatter off of thesubstrate is directed back towards, and captured by, the objective lensof the imaging device. This has the effect of blurring and washing outthe resulting image. It is known to enhance the resolution of brightfield microscopy by applying an anti-reflective coating to thesubstrate. This method, however, is expensive and requires that thetarget semiconductor chip be subjected to one or more additionalprocessing steps. It is also known to use laser scanning confocalmicroscopy to achieve higher resolution images through semiconductorsubstrates. Although laser scanning confocal microscopy does producegood results, the equipment for implementing it is extremely expensive,limiting its practical usefulness.

FIGURES

Various embodiments of the present invention are described here by wayof example in conjunction with the following figures, wherein:

FIG. 1 illustrates a cross-sectional view of one embodiment of an objecthaving subsurface features that may be imaged utilizing theside-addressed illumination techniques described herein.

FIG. 2 illustrates one embodiment of a system for side-addressedimaging.

FIG. 3 illustrates one embodiment of the object of FIG. 1 illuminated byan illumination beam.

FIG. 4 illustrates one embodiment of the object of FIG. 1 illuminated bythe beam oriented at an angle relative to normal of the surface of theobject.

FIG. 5 illustrates one embodiment of the object of FIG. 1 showing a beamreflected off of the surface of a feature of the object over a range ofangles.

FIG. 6 illustrates one embodiment of the object and feature shown inFIG. 1 showing rays reflected by a point on the feature.

FIG. 7 illustrates one embodiment of the object of FIG. 1 showing twosubsurface features and the ray reflections there from.

FIG. 8 illustrates a cross-sectional view of one embodiment of anotherobject having surface features that may be observed utilizing the sideaddressed illumination techniques described herein.

FIG. 9 shows one embodiment of the system of FIG. 2 including theimaging device, the object, the illumination source, and illuminationdirecting elements such as, for example, the fiber optic bundle andcollimating lens of FIG. 2.

FIG. 10 illustrates a closer view of the object as illustrated in FIG.9.

FIG. 11 illustrates a top view of the surface of the object of FIG. 10showing an illumination array comprising four different illuminationbeams.

FIG. 12 illustrates one embodiment of a system comprising anillumination array that includes multiple illumination sources and acomputer for optimizing an illumination pattern of the illuminationarray.

FIG. 13 illustrates a bottom-up view of one embodiment of the objectiveand illumination array showing one example configuration of theillumination sources.

FIGS. 14-15 illustrates example embodiments of the objective andillumination array of FIG. 13 showing example illumination patterns inwhich illumination sources are activated.

FIG. 16 illustrates a bottom-up view of one embodiment of the objectiveand illumination array of FIG. 12 illustrating an asymmetricalconfiguration of illumination sources.

FIG. 17 illustrates a flow chart showing one embodiment of a processflow that may be executed by the computer of FIG. 12 to select one ormore optimal illumination patterns.

FIG. 18 a illustrates an image that may be imaged by the system of FIG.12.

FIG. 18 b illustrates a region-of-interest (ROI) selected from the imageof FIG. 18 a.

FIG. 19 a illustrates the ROI of FIG. 18 a indicating an examplelineout.

FIG. 19 b illustrates an intensity plot of the horizontal lineout ofFIG. 19 a.

FIG. 20 illustrates an image that may be imaged by the system of FIG. 12taken under an optimized illumination pattern determined according tothe process flow of FIG. 17.

FIG. 21 illustrates a flow chart showing one embodiment of a processflow that may be executed by the computer of FIG. 12 to select one ormore optimal illumination patterns according to an evolutionaryalgorithm.

FIG. 22 is a block diagram of a set of parent illumination patterns anda set of child illumination patterns generated utilizing a two-pointcrossover operator.

DESCRIPTION

Various embodiments are directed to systems and methods for optimizingillumination for imaging, including for side-addressed imaging, asdescribed herein. Images of an object may have different qualitiesdepending on the lighting or illumination conditions that are present.Example illumination conditions include, for example, the number ofillumination sources present, the direction from which the illuminationsource is directed to the object, the intensity of illumination receivedfrom the illumination source, etc. The optimal illumination conditionsfor any given object may comprise different combinations of the exampleillumination conditions described herein.

In various embodiments, an illumination array comprises a plurality ofillumination sources. Each illumination source may be directed to theobject from a different direction. In some embodiments, differentillumination sources are directed to the object from different anglesrelative to a normal of the object surface. The illumination array maybe configurable to provide a plurality of different illuminationpatterns. For each distinct illumination pattern, a set of sources fromthe illumination array are illuminated. In some embodiments, anillumination pattern may also specify an intensity of illumination fromeach illumination source. For example, multiple illumination patternsmay involve illumination of the same illumination sources, albeit atdifferent combinations of intensities.

A computer or other processing device may be utilized to determine anoptimal illumination pattern for an object. Optimal illuminationpatterns may be selected by applying an optimization algorithm to a setof illumination patterns that are capable of being provided by theillumination array. An optimization algorithm may be applied todetermine one or more illumination patterns generating the highestquality image, referred to herein as optimal illumination patterns.Subsequent images of the object may be captured utilizing the one ormore optimal illumination patterns. The quality of images resulting fromany particular illumination pattern may be measured in any suitablemanner. In some embodiments, image quality may be measured by contrast.For example, images of an object exhibiting relatively higher contrastmay be considered superior to images exhibiting lesser contrast.

Any suitable optimization algorithm may be used to generate optimalillumination patterns. For example, in some embodiments, a set ofpotential illumination patterns is generated and/or received by acomputer or other processing device. The computer may instruct theimaging device and/or illumination array to capture images of the objectwith each of the set of potential illumination patterns activated by theillumination array. The resulting images may be evaluated using afitness function to determine a quality of each image. The quality ofeach image may be utilized to generate a fitness function value of thecorresponding illumination pattern. Based on the fitness function valuesfor each illumination pattern, a new set of potential illuminationpatterns may be generated. New fitness function values for the newillumination values may be determined, as described. Additional sets ofpotential illumination values may be generated in the manner described.Continued iterations may be performed until one or more optimalillumination patterns are converged upon by the algorithm. Exampleoptimization algorithms that may be used to determine the optimalillumination pattern or patterns include global search algorithms(GSA's) such as evolutionary algorithms (e.g., genetic algorithms,evolutionary programming, gene expression algorithms, evolutionstrategy, differential evolution, neuroevolution, learning classifieralgorithms, etc.) and swarm intelligence algorithms (e.g., ant colonyoptimization, bees algorithm, cuckoo search, particle swarmoptimization, firefly algorithm, invasive weed algorithm, harmonysearch, Gaussian adaptation, etc.).

The illumination optimization systems and methods described herein maybe utilized in any type of imaging including, for example, bright-fieldmicroscopy, laser scanning confocal microscopy, side-addressedillumination imaging, etc. Examples of side-addressed illuminationimaging that may be utilized in conjunction with the illuminationoptimization described herein are provided in the followingcommonly-owned United States patents and applications, which areincorporated herein by reference in their entireties: (1) U.S. Pat. No.8,138,476 to La Lumondiere, et al., issued on Mar. 20, 2012; (2) U.S.Pat. No. 8,212,215 to La Lumondiere, et al., issued on Jul. 3, 2012; (3)U.S. Patent Application Publication No. 2011/0102615 by La Lumondiere,et al., filed on Mar. 26, 2010; and (4) U.S. Patent ApplicationPublication No. 2012/0019707 by La Lumondiere, et al., filed on Jul. 25,2011.

FIGS. 1-7 provide a description of the operation of side-addressedillumination. It will be appreciated, however, that the illuminationoptimization techniques described herein may be used with other imagingtechniques as well. FIG. 1 illustrates a cross-sectional view of oneembodiment of an object 102 having an outer surface 108 and subsurfacefeatures 104, 106 that may be imaged utilizing the side-addressedillumination techniques described herein. The material 110 of the object102 between the subsurface features 104, 106 and the surface 108 mayhave an index of refraction at the imaging wavelength range that isgreater than that of the surrounding medium 109, which may be air. Thetechniques and apparatuses described herein may be used to imagesubsurface features in many contexts. In various embodiments, however,the object 102 may comprise a semiconductor substrate and the features104, 106 may be components such as transistors, diodes, resistors,metallization lines and/or other components formed from or on thesubstrate of the object 102. In this case, the imaging wavelength rangemay comprise some or all of the near infrared range, which istransparent in silicon. The ratio of the indices of refraction of thematerial 110 over the surrounding medium 109 (e.g. air) may beapproximately 3.5.

It will be appreciated that, when the object 102 is a semiconductordevice, the material 110 may be any suitable semiconductor materialincluding, for example, silicon, gallium arsenide (GaAs), siliconcarbide (SiC), and/or diamond. In some embodiments, the object 102 maybe mounted in a flip-chip manner. Accordingly, the features 104, 106 maybe visible through the remainder of the object 102 (e.g., thesubstrate). As viewed through the material 110, the features 104, 106may be below the surface of the object 102 by any suitable distance dthat permits transmission of illumination from an illumination sourceand reformation of an image by the objective or the objective lens of animaging device (see FIG. 2). In some embodiments, the distance d may be700 microns.

FIG. 2 illustrates one embodiment of a system 200 for side-addressedimaging. The system 200 includes an illumination source 202 opticallycoupled to a fiber optic bundle 204 (e.g., made of quartz or othersuitable material) and a collimating lens 206. According to variousembodiments, the source 202 may comprise a tungsten halogen lamp with agold-plated reflector. It will be appreciated that suitable systems mayomit various components such as the fiber optic bundle 204 andcollimating lens and/or incorporate some or all of these components intothe illumination source 202 itself. Light emitted by the source 202 maybe incident on, and may traverse, the fiber optic bundle 204 andcollimating lens 206 resulting in a beam 208 incident on the object 102at an angle offset from the surface normal. Although the source 202 isillustrated as emitting a collimated beam, it will be appreciated thatan uncollimated source may be used as well. An objective lens orobjective 212 may be positioned approximately along a normal of theobject 102 and may direct reflected portions of the beam 208 towards animaging device 214. The objective 212 may comprise a lens or combinationof lenses and/or apertures. The lens or lenses of the objective 212 maycomprise a standard lens or, in various embodiments, may comprise aconfocal lens for generating three dimensional images. According tovarious embodiments, the objective 212 may comprise a lx relay optic anda an NIR 50× long working distance objective, available from MITUTOYO.

The imaging device 214 may comprise any suitable camera or other imagingelement capable of sensing the imaging wavelength range. For example, asshown, the imaging device 214 may comprise a 320×240 Indium GalliumArsenide (InGaAs) array, such as a GOODRICH SU320 sensor with 25 μmpixel pitch. The combination of the MITUTOYO NIR 50× objective 212 andthe GOODRICH SU320 sensor may yield a field-of-view of 300 μm×200 μm. Itwill be appreciated, however, that different sensor sizes and objectivecomponents may be used to generate any suitable field of view. Theimaging device 214 may capture an image and display it on a monitor 215or similar visual display device. In addition to, or instead of,displaying the image on the monitor 215, the imaging device 214 maystore captured images at a computer readable medium (not shown), such asread only memory (ROM), random access memory (RAM), a hard drive, aflash drive or other data storage device.

According to various embodiments the system 200 may utilize an imagingwavelength or wavelength range that is transparent, or near-transparent,relative to the material 110. For example, when backside imaging isperformed through a silicon substrate, the imaging wavelength range maybe selected to include wavelengths greater than about 1100 nm. Theimaging wavelength range may be implemented in any suitable way. Forexample, the source 202 may be a broadband source and one or moreoptical filters may be positioned in the optical path between the source202 and the imaging device 214. Also, for example, the source 202 may bea narrow-band source that emits only radiation in the imaging wavelengthrange. In addition to, or instead of these variations, the imagingdevice 214 may be a narrow band device that is sensitive only toradiation in the imaging wavelength range (e.g., an InGaAs imagingdevice 214 may be selected with a sensitivity between 900 nm and 1700nm). In some embodiments, the object 102 may serve as an optical filter.For example, when the object 102 is a silicon substrate and theillumination source 202 is a broadband source, the silicon substrate maytend to absorb all wavelengths other than the near-infrared wavelengths,which are reflected and refracted as described herein.

FIG. 3 illustrates one embodiment of the object 102 showing subsurfacefeature 104. The incident beam 208 is incident on the object 102 at anangle 304 relative to the surface normal 307. The angle 304 may be setbased on the position and orientation of the illumination source 202.The angle 304 may be selected such that specular reflection of the beam208 off of the object 102 falls outside of an acceptance angle 306 ofthe objective 212. For example, the angle 304 may be at least equal tothe acceptance angle 306 of the objective 212 and less than 90°. It willbe appreciated that as the angle 304 increases, the intensity of thelight source 202 may also need to be increased to compensate forincreasing portions of the illumination beam 208 being reflected off ofthe object 102 out of the view of the objective 212.

In practice, reflection from the object 102 may not be perfectlyspecular (e.g., the surface 108 may not be perfectly smooth).Accordingly, the beam 208 may scatter off of the object 102 at a rangeof angles represented by cone 308. To compensate for this effect, theangle 304 may be selected to be slightly larger than the acceptanceangle of the objective 212 such that the actual reflection of the beam208 off of the object 102 falls substantially outside of the acceptanceangle 306 of the objective 212. In this way, the image noise due tosurface reflection may be minimized. In one example embodiment where theobject 102 is a silicon substrate, the angle 304 may be 45°.

A portion of the beam 208 may be transmitted through the interfacebetween the surrounding medium 109 (e.g., air) and the object 102. Dueto the differing indices of refraction between the surrounding medium109 and the material 110, the resulting light will be refracted towardsthe normal direction. Also, because the surface 108 of the object 102may not be perfectly smooth, the refracted portion of the beam 208 maybegin to spread, as represented by cone 312. The refracted portion 312may be incident on and illuminate the feature 104 for imaging.

FIG. 4 illustrates one embodiment of the object 102 illuminated by thebeam 208 oriented at the angle 304 relative to normal of the surface ofthe object 102 (represented by normal dashed lines 402, 403). At theinterface between the object 102 and the surrounding medium 109, thebeam 208 may be refracted such that its angle relative to the normal 402is shown by 404. When the surrounding medium 109 is air (index ofrefraction˜1), the object 102 is a silicon substrate (index ofrefraction˜3.5) and the angle 304 is about 45°, given Snell's law, theangle 404 may be about 11.6°. After entering the object 102, theincident beam 208 may reflect off of the feature 104, resulting in areflected beam 401. The reflected beam 401 may be incident on thesurface 108 between the object 102 and the surrounding medium 109 at theangle 404 relative to the normal 403. At the surface 108, the reflectedbeam 401 may be refracted to the angle 304 relative to the normal 403.

It can be seen that, as illustrated in FIG. 4, the reflected beam 401 isnot incident on the objective 212 within its acceptance angle 306. Atleast two factors, however, allow portions of the beam 401 to beincident on the objective 212. First, as illustrated in FIG. 3,roughness on the surface 108 of the object 102 may cause the incidentbeam 208 to actually be incident on the feature 104 over a range ofangles, represented by cone 312 shown in FIG. 3. Further, surfaceroughness of the feature 104 may cause the reflected beam 401 to bescattered over a range 502 of angles, including angles that allow aportion of the reflected beam 401 to be incident on the objective 212within its acceptance angle (see FIG. 5). It will be appreciated thatportions of the beam 401 follow paths similar to those shown in FIG. 4and, therefore, such portions are not incident on the objective 212.Because a portion of the reflected beam 401 is lost, it may be desirableto choose an illumination source 202 having an intensity relativelygreater than what would be used for a similar bright field imagingset-up. For example, in various embodiments, the intensity of theillumination source 202 may be an order of magnitude larger than thatused for a similar bright field imaging set-up.

According to various embodiments, refraction at the interface betweenthe surface 108 and the surrounding medium 109 may serve as a spatialfilter, increasing the resolution of the image captured by the objective212 by minimizing the spatial distribution of beams captured from eachpoint of the feature 104. This effect, which can be thought of as aninverse of the Snell's window effect observed under water, isillustrated in FIG. 6. FIG. 6 shows one embodiment of the object 102 andfeature 104 including rays 602, 604, 606 reflected by a point 600 on thefeature 104. The ray 602 is incident on the surface/surrounding medium109 interface at an angle within the acceptance range of the objective212. Accordingly, the ray 602 is received by the objective 212 andtransmitted to the imaging device 214 (see FIG. 2). The rays 604 and606, in contrast, are outside of the acceptance range. As shown byun-refracted paths 614, 616, the rays 604, 606 would ordinarily beincident on objective 212 within its acceptance angle. Because ofrefraction, however, the rays 604, 606 are bent outside of theacceptance angle of the objective 212. As a result, the minimum spacingbetween subsurface objects 104 and 106 that can be resolved is based onthe wavelength of the incident light 208 divided by the index ofrefraction of the substrate material 102, thus improving imageresolution.

The utility of the spatial filtering effect is demonstrated by FIG. 7,which shows one embodiment of the object 102 showing both of thesubsurface features 104, 106. Rays 706, 710 and 712 are reflected off ofa point 704 on feature 104. Rays 708, 714 and 716 are reflected off of apoint 702 on feature 106. As illustrated, rays 706 and 708 are withinthe acceptance range and, therefore, are incident on the objective 212.Rays 710, 714, 712 and 716, after refraction at the interface betweenthe object 102 and the surrounding medium 109, are outside of theacceptance range and, therefore, are not incident on the objective 212.Dashed lines 720, 724, 722, 726 indicate the paths of the respectiverays 710, 714, 712, 716 absent refraction at the object 102/surroundingmedium 109 interface. It will be appreciated that, but for therefraction, ray 714 from point 702 would overlap ray 706 from point 704.This would result in fuzziness and lack of clarity in the resultingimage (e.g., in the image, the border between feature 104 and feature106 would be blurred). As shown, however, the refraction between theobject 102 and the surrounding medium 109 minimizes beam overlap fromnearby points, thus improving image quality.

Also, for example, the apparatuses and methods described herein may beused to image features on the surface of an object by providing atemporary or permanent layer of high refractive index material over thesurface prior to imaging. For example, FIG. 8 illustrates across-sectional view of one embodiment of another object 800 havingsurface features 802. The surface features 802 may be imaged byproviding a layer 804 of material having a high refractive index at theimaging wavelength range. The layer 804 may be deposited onto the object800 using any suitable deposition technique. According to variousembodiments, the layer 804 may be a fluid, such as an optical couplingfluid, that may be applied to the object 800 in any suitable manner. Thelayer 804 may be permanent or removable.

FIGS. 9 and 10 illustrate one embodiment of the system 200 configured toprovide illumination from multiple directions (e.g., positions andangles). For example, FIG. 9 shows one embodiment of the system 200including the imaging device 214, the illumination source 202, andillumination directing elements such as, for example, the fiber opticbundle 204 and collimating lens 206. Illumination beam 208 is shownincident on an imaging location 902 of the surface 108 of the object102. Directions in FIG. 9 are indicated by the x, y and z axes shown.For example, the surface 108 may be in the x-y plane. The z-directionmay be normal to the surface 108. FIG. 10 illustrates a closer view ofthe object 102 as illustrated in FIG. 9. A surface normal 1002 isillustrated normal to the surface 108 in the direction of the z-axis.For example, the surface normal 1002 may be directed from the surface108 to the objective 212, as shown.

FIG. 11 illustrates a top view of the surface 108 showing anillumination pattern 1101 comprising illumination beams 1102, 1104,1106, 1108. Each of the illumination beams 1102, 1104, 1106, 1108 may beincident on the imaging location 902 at an angle relative to the normal1002. In some embodiments, all of the illumination beams 1102, 1104,1106, 1108 are incident on the imaging location 902 at the same anglerelative to the normal 1002. In other embodiments, differentillumination sources 1102, 1104, 1106, 1108 are incident on the imaginglocation 902 at different angles relative to the normal 1002. Asillustrated, each of the illumination beams 1102, 1104, 1106, 1108 isrotated about the normal 1002 in the x-y plane at various anglesrelative to one another. For example, the illumination beams 1104 and1108 are shown rotated from the beam 1102 by +90° and −90°,respectively. The beam 1106 is shown similarly rotated from the source1102 by 180°.

The various illumination beams 1102, 1104, 1106, 1108 may be generatedby multiple, distinct illumination sources. In some embodiments,however, the beams 1102, 1104, 1106, 1108 are generated by a singleillumination source that may be rotated or otherwise directed to theposition of each beam 1102, 1104, 1106, 1108 shown in FIG. 11. Althoughfour illumination beams 1102, 1104, 1106, 1108 are shown, it will beappreciated that additional beams may be omitted or added. For example,in some embodiments, it may be desirable to have three beams. A firstbeam may be considered to be positioned at 0°. A second beam may berotated about the normal 1002 by +45°, and a third beam may be rotatedabout the normal 1002 by −45°. Also, FIGS. 13-15, described hereinbelow, describe an example embodiment comprising twelve distinctillumination sources capable of generating twelve distinct illuminationbeams.

In some embodiments of the configuration illustrated in FIG. 11, all ofthe beams 1102, 1104, 1106, 1108 are illuminated at the same time. Inthis case, a single image of the imaging location 902 may be capturedwith all of the illumination beams 1102, 1104, 1106, 1108 active. Insome embodiments, however, less than all of the beams 1102, 1104, 1106,1108 are illuminated at the same time. For example, in some embodiments,the beams 1102, 1104, 1106, 1108 may be illuminated separately or in acombination of less than all of the beams 1102, 1104, 1106, 1108 (e.g.,illumination pattern). An optimal illumination pattern may bedetermined, for example, as described herein. Also, in some embodiments,a separate image may be captured with each of a plurality ofillumination patterns. The resulting images may be utilized in theprocess of determining an optimal illumination pattern. In someembodiments, some or all of the resulting images are composted orotherwise combined to form a composite image.

According to various embodiments, the illumination pattern for aparticular object 102 may be selected based on the orientation of thesurface 108 and any sub-surface features 104. For example, illuminatinga surface 108 in a direction parallel to and in a directionperpendicular to sub-surface features 104, in some embodiments, providesincreased resolution. When the object 102 is a semiconductor chip, thesub-surface features 104 may be arranged in a grid-like Manhattan-styleconfiguration. Accordingly, at least two illumination beams may bedirected at the imaging location 902, with the beams aligned with thegrid of the sub-surface features 104 and separated from one anotherabout the normal 1002 by 45°. When X-architecture chips or othernon-Manhattan-style objects are imaged, different illumination beamdirections may be selected to illuminate the parallel and perpendiculardirections of major sub-surface features 104.

In some embodiments, however, the pattern of sub-surface or otherfeatures in or on an object may be too complicated to be optimallyilluminated by the simple parallel and perpendicular illuminationpattern described above. For example, some sub-surface features may berounded rather than flat, some sub-surface features may be non-parallel,etc. Accordingly, an illumination array may comprise a plurality ofillumination sources that may be utilized in conjunction with a computeror other processing device to determine an optimal illumination pattern.FIG. 12 illustrates one embodiment of a system 1200 comprising anillumination array 1202 that includes multiple illumination sources 1206and a computer 1208 for optimizing an illumination pattern of theillumination array 1202. As illustrated, the system 1200 comprises anobjective 212 and imaging device 214, for example, as described above.The illumination array 1202 is positioned to provide illumination forimages of the object 102 captured by the imaging device 214. Theindividual illumination sources 1206 may be secured to a bracket 1208 orany other suitable frame or frames for fixedly securing the sources1206. In some embodiments, the sources 1206 are movable. For example,the sources 1206 may rotate about the normal 1002 and/or may changeangles relative to the normal 1002. In some embodiments, the sources1206 are movable via servo motors and/or any other type of motion device(not shown), which may be under the control of the computer 1208. Insome embodiments, the object 102, surrounding medium 109 and the angleof one or more of the sources 1206 relative to the normal 1002 areconfigured as described above to achieve the advantages ofside-addressed illumination.

The computer 1208 may be programmed to implement an illumination patternoptimization algorithm, as described herein. For example, the computer1208 may be in electronic communication with the imaging device 214 viaany suitable communication bus utilizing any suitable protocol (e.g.,universal serial bus (USB), etc.). The computer 1208 may be capable ofinstructing the imaging device 214 to capture an image of the imaginglocation 902 (as shown in FIG. 9). The computer 1208 may also be capableof receiving images captured by the imaging device 214. The capturedimages may be received in any suitable image file format including,Widows bitmap or BMP file format, Joint Photographic Experts Group(JPEG) format, Tagged Image File Format (TIFF) format, GraphicsInterchange Format (GIF), etc. The computer 1208 may also be inelectronic communication with the illumination array 1202. For example,the computer 1208 may be capable of instructing the array 1202, and thevarious sources 1206 thereof, to illuminate according to variousdifferent illumination patterns.

FIG. 13 illustrates a bottom-up view of one embodiment of the objective212 and illumination array 1202 showing one example configuration of theillumination sources 1206. In FIG. 13, twelve, (12) illumination sources1206 are positioned at different angles in the x-y plane relative to theobjective 212 around the normal 1002. (The normal 1002 is illustrated inFIG. 13 in conjunction with the x-y axis marker so as to avoid obscuringthe objective 212.) It will be appreciated that different objects 102may be optimally illuminated by different combinations of theillumination sources 1206. FIG. 14-15 illustrate example embodiments ofthe objective 212 and illumination array 1202 of FIG. 13 showing exampleillumination patterns in which illumination sources 1206′ are activated.The activated sources 1206′ may provide illumination at a commonintensity and/or at different intensities. It will be appreciated thatthe illumination sources 1206 of the illumination array may be arrangedin any suitable configuration. For example, FIG. 16 illustrates abottom-up view of one embodiment of the objective 212 and illuminationarray 1202 of FIG. 12 illustrating an asymmetrical configuration ofillumination sources 1206. The illumination sources 1206 as illustratedin FIG. 16 are not symmetrical about the x and y axis, though anysuitable asymmetry may be used.

Referring back to FIG. 12, the computer 1208 may be utilized to deriveone or more optimal illumination patterns that may be delivered by theillumination array 1202 for imaging of the object 102. Any suitableoptimization algorithm may be used. FIG. 17 illustrates a flow chartshowing one embodiment of a process flow 1700 that may be executed bythe computer 1208 to select one or more optimal illumination patterns.The process flow 1700 demonstrates the implementation of a global searchalgorithm, such as an evolutionary algorithm or a particle swarmoptimizer algorithm, sometimes referred to as a swarm algorithm. At1702, the computer 1208 may generate and/or receive a first result set.The result set may comprise one or more illumination patterns (e.g.,patterns capable of being generated by the illumination array 1202). Theillumination patterns included in the first result set may be selectedin any suitable manner. For example in some embodiments, theillumination patterns for the first result set are randomly selected. Insome embodiments, the illumination patterns for the first result set areselected by a human operator based on “dead reckoning” or a best guessof what the optimal illumination pattern will be.

At 1704, the computer 1208 may evaluate a fitness function for each ofthe illumination patterns included in the first result set. For eachillumination pattern, the computer 1208 may instruct the illuminationarray 1202 to illuminate according to the illumination pattern. With theillumination pattern implemented by the illumination array 1202, thecomputer 1208 may instruct the imaging device 214 to capture an image ofthe object 102. The resulting image may be evaluated in any suitablemanner to determine a fitness function value of the illuminationpattern. The fitness function value may be calculated for the entireimage and/or for a region of interest (ROI) of the image that may beselected by an operator of the system. In some embodiments, described inmore detail below, the fitness function is evaluated considering anintensity contrast of the image.

Upon finding a fitness function for each of the illumination patterns ofthe first result set, the computer 1208 may determine, at 1706, if anyof the fitness functions are at a defined threshold. The threshold maydefine a point where an illumination pattern associated with a fitnessfunction is either the optimal illumination pattern (e.g., the algorithmhas converged) and/or is within a desired tolerance of the optimalillumination pattern. If the fitness function of at least one of theillumination patterns is at or above the threshold, then the algorithmmay be considered to have converged at 1708. The illumination pattern orpatterns having fitness functions greater than or equal to the thresholdmay be considered an optimal illumination pattern or pattern for thecombination of the system 1200 and object 102 and may be used forsubsequent images of the object 102 using the system 1200. If none ofthe calculated fitness functions reach the threshold, the computer 1208may, at 1710, generate a new result set based on the first result set(and/or the calculated fitness function values). The new result set mayrepresent an additional iteration or generation of the algorithm. At1704, the computer may calculate fitness function values for the newresult set. In some embodiments, the process continues until thecomputer 1208 identifies an optimal illumination pattern (e.g.,converges) and/or is within a desired tolerance of the optimalillumination pattern.

The fitness function calculated above at 1704 may be any suitablefunction yielding a value or values that indicate the quality of themeasured illumination pattern as expressed, for example, by the qualityof the resulting image. In various embodiments, the fitness functionyields a single number value that acts as a quantitative measure of thequality of the measured illumination pattern. Any fitness functioncapable of yielding fitness function values that discern differencesbetween different lighting conditions may be used. For example differentacceptable fitness functions may measure different aspects of imagequality. One type of fitness function may measure the intensity contrastof an image. Other types of fitness functions may utilize edgeenhancement algorithms, matching filters, recognition algorithms, etc.

Equation (1) below provides an example fitness function based on themodulation contrast of a structure that has alternating light and darkareas:

$\begin{matrix}{C = \frac{I_{\min} - I_{\max}}{I_{\min} + I_{\max}}} & (1)\end{matrix}$

In Equation (1), C is contrast and I_(min) and I_(max) are minimum andmaximum intensities of the object 102 (e.g., the minimum and maximumpixel values indicated in the image of the object 102). In someembodiments, the process flow 1700 is executed to optimize the contrastC in order to maximize the imaging contrast of the subsurface features104 of the object 102.

When the illumination optimization systems and methods described hereinare implemented in the context of a microscope system, it will beappreciated that some sub-surface features 104 may be small enough toexceed a maximum resolution of the imaging system. In such cases, amodulation transfer function (MTF) of the optical system may bequantified. The MTF is a measure of the maximum contrast (C) that can bemeasured by an optical system for a given spatial periodicity ofalternating light and dark structures (e.g., the subsurface features 104of the object 102, substrate, etc.). For semiconductor applications, thedistance between alternating light and dark regions is typicallymeasured in lines per millimeter (lines/mm). As the spacing between darkand light elements becomes smaller, the C between the elements decreasesuntil the optical system can resolve only a grey line. The MTF for theoptical system may indicate the minimum observable distance betweenlight and dark structures before the optical system returns such a greyline.

In some embodiments utilizing a modulation contrast as a fitnessfunction, the fitness function threshold is reached when, the maximumachievable C according to the measured MTF of the optical system isreached, the measured C does not change after a predetermined number ofalgorithm generations and/or when the measured C has met a predeterminedvalue (e.g., a user predetermined value). In some embodiments, theoptical system (e.g., imaging device 214 and objective 212) may have itsMTF measured before optimization. Values for the MTF may be stored, forexample, at the computer 1208 as a look-up file or in any other suitabledata structure.

In one example use case, the object 102 may comprise a structure on amicroelectronic device having two metal lines separated by 1 μm withsemiconductor material between them. Under illumination, as describedherein, the metal lines reflect illumination and are “light,” while thesemiconductor passes or absorbs the illumination and are “dark.” Anoperator of the system 1200 may select the metal lines as aregion-of-interest (ROI) in the resulting image. FIG. 18 a illustratesan image 800 that may be imaged by the system of FIG. 12. The image 800shows an ROI 802 that may be selected by the operator. The ROI 802,illustrated in more detail in FIG. 18 b, includes metal lines separatedby 1 μm, as described above. In various embodiments, the ROI 802 may beselected at any suitable size. For example, it may be a portion of theimage, as shown, or the entire image. The computer 1208 may take amodulation contrast-related fitness function value (e.g., C) of the ROI802. For example, the computer 1208 may be configured to take onedimensional lineouts (e.g., entire rows or columns from the ROI 802 thatwill result in one dimensional arrays). The lineouts may be takenhorizontally and/or vertically. For each lineout, C may be calculatedconsidering the alternating light and dark regions. FIG. 19 aillustrates the ROI 802 of FIG. 18 a indicating an example horizontallineout 804. FIG. 19 b illustrates an intensity plot of the horizontallineout 804 of FIG. 19 a.

In various embodiments, an average C may be taken over the ROI 802. Insome embodiments, average C's are calculated for both the horizontal andvertical lineouts. Separate horizontal and vertical C's may beconsidered separately and/or averaged to find a single C (in thisexample, the fitness function value) for the ROI 802. The process may berepeated to find fitness function values for images taken underdifferent illumination patterns, as described with respect to FIG. 17.In some embodiments, a maximum limit on image intensity (e.g., theintensity of each pixel) may be set so as to avoid saturating theimaging device 214. FIG. 20 illustrates an image 900 that may be imagedby the system of FIG. 12 taken under an optimized illumination patterndetermined according to the process flow 1700 of FIG. 17. The image 900depicts the same semiconductor and metal lines as shown in FIGS. 18-19.

As described above, any type of optimization algorithm may be utilizedby the computer 1208 to determine an optimal illumination pattern orpatterns for the illumination array 1202 to illuminate a particularobject 102. FIG. 17, described above, illustrates a genericimplementation of a global search algorithm. It will be furtherappreciated that the computer 1208 may implement any suitable globalsearch algorithm. For example, in some embodiments, the computer 1208selects an optimal illumination pattern or patterns according to anevolutionary algorithm, a particle swarm optimizer algorithm, or ahybrid algorithm. FIG. 21 illustrates a flow chart showing oneembodiment of a process flow 2100 that may be executed by the computer1208 of FIG. 12 to select one or more optimal illumination patternsaccording to an evolutionary algorithm. At 2102, an initial populationof illumination patterns may be generated. The initial population,similar to the first result set at 1702 above, may be generated in anysuitable manner. In some embodiments, the initial population isgenerated randomly (e.g., by the computer 1208). In some embodiments,the initial population is generated randomly with a given distributionsize determined by a Gaussian distribution. In some embodiments, theinitial population is generated around a solution (or what is believedto be close to the optimal illumination pattern). The initial populationmay be evaluated utilizing a fitness function at 2104. For example, 2104may correspond to the action 1704 from the process flow 1700.

After application of the fitness function, the initial population may besorted at 2106, for example, in order of fitness function value. At2108, the computer 1208 may select a set of one or more illuminationpatterns for mating (e.g., a mating set). The selected illuminationpatterns may include patterns having high fitness function values. Forexample, in some embodiments, the top N percent of illumination patternsby fitness function values are selected, where N is any suitable value.At 2110, the computer 1208 may generate child illumination patterns fromthe mating set. For example, action 2110 may correspond to action 1710of the process flow 1700 described herein above. At 2110, the childillumination patterns may be generated using any suitable operator oroperators. In some embodiments, the number of illumination patterns inthe mating set and the number of child illumination patterns selected bythe operator or operators may be weighted or otherwise predetermined atthe beginning of optimization so that the number of individualillumination patterns analyzed remains constant from one generation tothe next.

In one example embodiment, three operators may be used including amutation operator, a two-point crossover operator and an elitismoperator. The mutation operator may randomly change a portion of anindividual illumination pattern (e.g., the intensity of illuminationprovided by different sources 1206 of the illumination array 1202) withsome small frequency. A two-point crossover operator may swap splicedportions of parent illumination patterns to generate child illuminationpatterns. FIG. 22 is a block diagram of a set of parent illuminationpatterns 2152, 2154 and a set of child illumination patterns 2156, 2158generated utilizing a two-point crossover operator. Each of theillumination patterns 2152, 2154, 2156, 2158 may comprise a series ofnumbers where each number corresponds to an intensity of illuminationprovided by sources 1206 of the array 1202. Each of the parent patterns2152, 2154 may be split at two positions 2160, 2162. Pattern valuesbetween the two positions may be swapped, generating the two new childillumination patterns 2156, 2158.

According to an elitism operator, a set of one or more best individualillumination patterns may pass unchanged from one generation to thenext. The best individual illumination patterns may be the patterns fromthe mating set that have the highest fitness function values. The numberof best individual illumination patterns may be selected in any suitableway and, in some embodiments, may be predetermined. In some embodiments,an elitism operator serves to prevent the algorithm from mutating awayfrom the best values that it has already discovered (e.g., gettinglost). The elitism operator may be most value in early generations of anoptimization and, in some embodiments, is utilized only in the earlygenerations of an optimization (e.g., for the first N generations).

Other example operators that maybe used include an average crossover,one-point crossover, smooth creep, one point blend, islanding, etc. Asmooth operator may make incremental changes to parent illuminationpatterns to smooth out discontinuous portions of the pattern. A creepoperator may make incremental changes to parent illumination patterns toslightly change its values. An average crossover operator may averagethe values of two illumination patterns together to generate a singlechild pattern. A one point blend operator may be similar to an averagecrossover value, but for just a single value of an illumination pattern.An islanding operator may create multiple instances of subpopulationswith periodic migrations of the strongest individual patterns betweenislands. Referring back to FIG. 21, it will be appreciated thatgenerating child illumination patterns at 2110 may utilize any of theoperators described herein, as well as other operators, in any suitableconfiguration or pattern. In various example embodiments, all individualillumination patterns are considered for reproduction via each operator.The probability that an illumination pattern will be chosen forreproduction may be determined either by random, or by using a fitnessdependent weighting criteria that gives more fit illumination patterns abetter chance at reproducing than less fit illumination patterns. At2114, the computer 1208 may evaluate the fitness function values for thechild illumination sets generated at 2110. If one or more of the childillumination sets converge at 2116, the algorithm may be complete. Ifnot, an additional generation of the illumination sets may be generated(e.g., beginning at 2106).

In some example embodiments, as described herein above, a GSA algorithmmay be and/or utilize aspects of a particle swarm optimization algorithmor swarm algorithm. Particle swarm optimization algorithms are modeledafter the swarming characteristics of bees and other similar creatures.Initially, a set of “particles” are randomly distributed in a searchspace. Each particle may correspond to an illumination set from thefirst result set (See FIG. 17). Each particle may be provided with arandom velocity, where the velocity indicates a direction and speed ofchanges made to the particle. As each particle moves a time step (e.g.,one iteration of the algorithm), the fitness function for theillumination set is found. A best position for each particle (e.g.,value of the particle when the best fitness function value is yielded)as well as a best global position (e.g., the illumination setcorresponding to the best fitness function value) is stored. Thevelocity of each particle may be updated considering one or more of thefollowing four parameters: a best individual position of the particle(p_(id)); a globally best position (p_(g)); a cognitive constant Φ_(c);and a social constant Φ_(s). In some embodiments, these parameters maybe utilized to update the velocity of each particle according toEquation (2) below:

v _(i+1) =v _(i)+φ_(c)(p _(id) −x _(i))+φ_(s)(p _(g) −x _(i));  (2)

In Equation (2), x_(i) is a current position of the particle. A newposition after a time step is given by Equation (3) below:

x _(i+1) =x _(i) +v _(i+1)  (3)

Simple Newtonian expressions may be used to update the particle positionand velocity until convergence is obtained.

Also, in some embodiments, a GSA algorithm may be and/or utilize ahybrid algorithm having aspects of a genetic algorithm and a particleswarm optimizer algorithm. Such hybrid algorithms may retain positiveaspects of both genetic algorithms and particle swarm optimizeralgorithms and may lead to efficiently determined optimal solutionsregardless of problem structure. In one example embodiment, the computer1208 may run a genetic algorithm for a threshold number of generationsand/or until a fitness function value or values reaches a predeterminedlevel, at which point a particle swarm optimization algorithm is useduntil convergence is reached.

According to an additional hybrid algorithm, a single initial set ofillumination patterns is used for concurrent implementations of agenetic algorithm and a particle swarm optimizer algorithm. Followingevery fitness evaluation (e.g., generation), each algorithm may beallowed to contribute to a new generation of individual illuminationpatterns. The magnitude of each algorithms contribution may bedetermined based on the performance of the algorithm. For example, insome embodiments, a set of patterns from one generation having thehighest fitness function value may be passed into the next generation ofboth algorithms regardless of the algorithm of origin.

According to another hybrid algorithm, two populations of illuminationpatterns are initially generated with both being equal in size. Onepopulation may be made up of genetic algorithm individuals while theother may contain particle swarm optimizer particles. Both algorithmsmay operate simultaneously and separately on the respective populations,allowing both algorithms to compete for a solution. In some embodiments,after a certain number of iterations, the particle swarm optimizeralgorithm particle with the worst fitness value may be replaced by acopy of the genetic algorithm individual having the best fitness value.This may serve as a controlled method of exchanging information betweenthe genetic algorithm and the particle swarm optimizer algorithm.

In some embodiments, the particular optimization algorithm used may beselected based on tests of multiple algorithms according to any suitableevaluation criteria. One such set of evaluation criteria is the De Jongtest suite. The De Jong test suite comprises five functions that arecommonly used to test the performance of optimization algorithms. Eachof the functions is designed to simulate a particular problem andprovide a straightforward method of evaluating an algorithm's ability toovercome certain difficulties in optimization. Optimization algorithmsfor illumination patterns, as described herein, may be evaluated usingany of the De Jong test functions. In some embodiments, though, theSphere function and Step function may be used. The Sphere function isgiven by Equation 4 below:

$\begin{matrix}{{f\; 1\left( x_{i} \right)} = {\sum\limits_{i = 0}^{n}x_{i}^{2}}} & (4)\end{matrix}$

The Step function is given by Equation (5) below:

$\begin{matrix}{{f\; 3\left( x_{i} \right)} = {\sum\limits_{i = 0}^{n}\left\lfloor x_{i} \right\rfloor}} & (5)\end{matrix}$

In Equations (4) and (5), x_(i) represents the genes or variables usedin the algorithm to represent the properties of illumination patterns.The variable n in Equations (4) and (5) represents the number of genesor variables that are used for the optimization.

In some embodiments, the computer 1208 may be programmed to implementmultiple optimization algorithms, for example, according to any of thedescriptions herein. Upon implementation, each of the algorithms may beevaluated using any suitable evaluation method including, for example,the Sphere and Step functions of the De Jong test suit reproduced above.Optimization algorithms having the strongest evaluation may bemaintained.

Various embodiments described herein may be modified to tilt thedirection of the objective away from the surface normal. For example, afirst image may be captured with the objective tilted off of the surfacenormal by a first angle. A second image may be captured with theobjective tilted off of the surface normal by a second angle. The twoimages may be combined, forming a composite image. According to variousembodiments, the direction of the objective at the first angle, thedirection of the objective at the second angle, and at least oneillumination beam may be coplanar.

Various embodiments described herein may be modified to discern areas ofa semiconductor component having different doping properties (e.g.,different bandgap energies). For example, the illumination source may beconfigured to generate illumination having a wavelength with anassociated photonic energy that is substantially equal to the bandgap ofa doped region of the semiconductor component. As a result, the dopedregion may attenuate the illumination causing the doped region to appeardark or shaded in the resulting image. Also, in some embodiments, thewavelength of the illumination source may be selected with a photonicenergy substantially equal to the bandgap of an un-doped region of thesemiconductor component, causing the un-doped region to appear dark orshaded. In various embodiments, the wavelength of the illuminationsource may be variable. For example, the illumination source may be setto various wavelengths corresponding to the bandgap energies ofdifferently doped regions in the semiconductor component. Each of thedifferently doped or un-doped regions may appear as a dark or shadedregion when the illumination corresponding to each region's bandgap isactive.

According to various embodiments, some or all of the embodimentsdescribed herein may also be used in conjunction with a polarizationtechniques. For example, a polarizer may be placed in an optical pathbetween the illumination source and the imaging device. The polarizermay be oriented with a polarization direction parallel to theillumination beam (e.g., perpendicular to the surface of the object). Inthis way, specular reflection off of the surface of the object mayeither be minimized (e.g., if the illumination beam is polarized) or itsdetection may be minimized (e.g., if the polarizer is placed in the pathof the imaging device).

Although the figures above are described in the context of backsideimaging of semiconductor devices, it will be appreciated that theapparatuses and methods disclosed herein may be used in various othercontexts as well. For example, the apparatuses and methods used hereinmay be used to image any subsurface features where the index ofrefraction of material between a surface of an object and subsurfacefeatures of the object is relatively greater than that of thesurrounding medium 109.

Various embodiments of computer-based systems and methods of the presentinvention are described herein. Numerous specific details are set forthto provide a thorough understanding of the overall structure, function,manufacture, and use of the embodiments as described in thespecification and illustrated in the accompanying drawings. It will beunderstood by those skilled in the art, however, that the embodimentsmay be practiced without such specific details. In other instances,well-known operations, components, and elements have not been describedin detail so as not to obscure the embodiments described in thespecification. Those of ordinary skill in the art will understand thatthe embodiments described and illustrated herein are non-limitingexamples, and thus it can be appreciated that the specific structuraland functional details disclosed herein may be representative andillustrative. Variations and changes thereto may be made withoutdeparting from the scope of the claims.

Reference throughout the specification to “various embodiments,” “someembodiments,” “one embodiment,” or “an embodiment,” or the like, meansthat a particular feature, structure, or characteristic described inconnection with the embodiment is included in at least one embodiment.Thus, appearances of the phrases “in various embodiments,” “in someembodiments,” “in one embodiment,” or “in an embodiment,” or the like,in places throughout the specification are not necessarily all referringto the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments. Thus, the particular features, structures, orcharacteristics illustrated or described in connection with oneembodiment may be combined, in whole or in part, with the featuresstructures, or characteristics of one or more other embodiments withoutlimitation.

In general, it will be apparent to one of ordinary skill in the art thatat least some of the embodiments described herein may be implemented inmany different embodiments of software, firmware, and/or hardware. Thesoftware and firmware code may be executed by a processor or any othersimilar computing device. The software code or specialized controlhardware that may be used to implement embodiments is not limiting. Forexample, embodiments described herein may be implemented in computersoftware using any suitable computer software language type, using, forexample, conventional or object-oriented techniques. Such software maybe stored on any type of suitable computer-readable medium or media,such as, for example, a magnetic or optical storage medium. Theoperation and behavior of the embodiments may be described withoutspecific reference to specific software code or specialized hardwarecomponents. The absence of such specific references is feasible, becauseit is clearly understood that artisans of ordinary skill would be ableto design software and control hardware to implement the embodimentsbased on the present description with no more than reasonable effort andwithout undue experimentation.

Moreover, the processes associated with the present embodiments may beexecuted by programmable equipment, such as computers or computersystems and/or processors. Software that may cause programmableequipment to execute processes may be stored in any storage device, suchas, for example, a computer system (nonvolatile) memory, an opticaldisk, magnetic tape, or magnetic disk. Furthermore, at least some of theprocesses may be programmed when the computer system is manufactured orstored on various types of computer-readable media.

It can also be appreciated that certain process aspects described hereinmay be performed using instructions stored on a computer-readable mediumor media that successful a computer system to perform the process steps.A computer-readable medium may include, for example, memory devices suchas diskettes, compact discs (CDs), digital versatile discs (DVDs),optical disk drives, or hard disk drives. A computer-readable medium mayalso include memory storage that is physical, virtual, permanent,temporary, semi-permanent, and/or semi-temporary.

A “computer,” “computer system,” “host,” “server,” or “processor” maybe, for example and without limitation, a processor, microcomputer,minicomputer, server, mainframe, laptop, personal data assistant (PDA),wireless e-mail device, cellular phone, pager, processor, fax machine,scanner, or any other programmable device configured to transmit and/orreceive data over a network. Computer systems and computer-based devicesdisclosed herein may include memory for storing certain software modulesused in obtaining, processing, and communicating information. It can beappreciated that such memory may be internal or external with respect tooperation of the disclosed embodiments. The memory may also include anymeans for storing software, including a hard disk, an optical disk,floppy disk, ROM (read only memory), RAM (random access memory), PROM(programmable ROM), EEPROM (electrically erasable PROM) and/or othercomputer-readable media.

In various embodiments disclosed herein, a single component may bereplaced by multiple components and multiple components may be replacedby a single component to perform a given function or functions. Exceptwhere such substitution would not be operative, such substitution iswithin the intended scope of the embodiments. Any servers describedherein, for example, may be replaced by a “server farm” or othergrouping of networked servers (such as server blades) that are locatedand configured for cooperative functions. It can be appreciated that aserver farm may serve to distribute workload between/among individualcomponents of the farm and may expedite computing processes byharnessing the collective and cooperative power of multiple servers.Such server farms may employ load-balancing software that accomplishestasks such as, for example, tracking demand for processing power fromdifferent machines, prioritizing and scheduling tasks based on networkdemand and/or providing backup contingency in the event of componentfailure or reduction in operability.

The computer systems may comprise one or more processors incommunication with memory (e.g., RAM or ROM) via one or more data buses.The data buses may carry electrical signals between the processor(s) andthe memory. The processor and the memory may comprise electricalcircuits that conduct electrical current. Charge states of variouscomponents of the circuits, such as solid state transistors of theprocessor(s) and/or memory circuit(s), may change during operation ofthe circuits.

It is to be understood that the figures and descriptions of the presentinvention have been simplified to illustrate elements that are relevantfor a clear understanding of the present invention, while eliminatingother elements, for purposes of clarity. Those of ordinary skill in theart will recognize that these and other elements may be desirable.However, because such elements are well known in the art and becausethey do not facilitate a better understanding of the present invention,a discussion of such elements is not provided herein.

In various embodiments disclosed herein, a single component may bereplaced by multiple components and multiple components may be replacedby a single component to perform a given function or functions. Exceptwhere such substitution would not be operative, such substitution iswithin the intended scope of the embodiments.

While various embodiments have been described herein, it should beapparent that various modifications, alterations, and adaptations tothose embodiments may occur to persons skilled in the art withattainment of at least some of the advantages. The disclosed embodimentsare therefore intended to include all such modifications, alterations,and adaptations without departing from the scope of the embodiments asset forth herein.

We claim:
 1. An imaging system for generating an image of a sub-surfacefeature of an object through a surface of the object, the systemcomprising: an illumination array, the illumination array comprising aplurality of illumination sources positioned around the sub-surfacefeature of the object, wherein each of the illumination sources directsillumination in an imaging wavelength range towards the surface at anangle relative to the normal of the surface; an imaging devicecomprising an objective; a computer system in communication with theillumination array, the computer system comprising at least oneprocessor and operatively associated memory, wherein the computer isprogrammed to: calculate an optimized illumination pattern of theplurality of illumination sources for imaging the sub-surface feature;activate the optimized illumination pattern; and instruct the imagingdevice to capture an image of the sub-surface feature with the imagingdevice based on reflected illumination from the optimized illuminationpattern.
 2. The system of claim 1, wherein the object is substantiallytransparent over the imaging wavelength range, and wherein the objecthas an index of refraction that is greater than an index of refractionof a surrounding medium that surrounds the object.
 3. The system ofclaim 1, wherein, for each of the plurality of illumination sources: theangle is greater than an acceptance angle of an objective of an imagingdevice; a first portion of the illumination is reflected at the surfaceand is not incident on the objective; a second portion of theillumination is refracted through the surface towards the sub-surfacefeature, is incident on the sub-surface feature and is reflected by thesub-surface feature back toward the surface over a range of angles; atleast a portion of the range of angles is within an acceptance angle ofthe objective, the second portion of the illumination is refracted atthe surface such that an attenuated component of the second portion isrefracted outside of the acceptance angle of the objective and afiltered component of the second portion is incident on the objective.4. The system of claim 3, wherein the imaging device is configured tocapture the image of the sub-surface feature based on the filteredcomponent of the second portion received by the objective.
 5. The systemof claim 1, wherein calculating the optimized pattern of the pluralityof illumination sources for imaging the sub-surface feature comprisesapplying a global search algorithm.
 6. The system of claim 1, whereincalculating the optimized pattern of the plurality of illuminationsources for imaging the sub-surface feature comprises applying anevolutionary algorithm.
 7. The system of claim 1, wherein calculatingthe optimized pattern of the plurality of illumination sources forimaging the sub-surface feature comprises applying a particle swarmoptimizer algorithm.
 8. The system of claim 1, wherein calculating theoptimized pattern of the plurality of illumination sources for imagingthe sub-surface feature comprises: receiving a first result set, whereinthe first result set comprises a plurality of illumination patterns; foreach of the plurality of illumination patterns of the first result set:receiving an image of the sub-surface feature of the object with theillumination pattern activated; and based on the image, calculating afitness function value; and determining whether the fitness functionvalue for any of the plurality of illumination patterns of the firstresult set exceeds a threshold value; conditioned upon none of thefitness function values for any of the plurality of illuminationpatterns of the first result set exceeding the threshold value,generating a next result set comprising a second plurality ofillumination patterns, wherein the generating is based at least in parton the first result set and the fitness function values for theplurality of illumination patterns of the first result.
 9. The system ofclaim 8, wherein, for at least a portion of the plurality of imagepatterns, the fitness function value for each of the plurality ofilluminated patterns is based on a region of interest of the image. 10.The system of claim 8, wherein calculating the optimized pattern of theplurality of illumination sources for imaging the sub-surface featurecomprises generating the first result set.
 11. The system of claim 8,wherein calculating the fitness function value comprises measuring anintensity contrast of the image.
 12. The system of claim 8, whereincalculating the fitness function value comprises measuring a modulationcontrast of a structure in the image.
 13. The system of claim 8, whereingenerating the next result set comprising a second plurality ofillumination patterns comprises: selecting a portion of the plurality ofillumination patterns of the first result set based on the fitnessfunction values of the plurality of illumination patterns; andgenerating a plurality of child illumination patterns based on theportion of the plurality of illumination patterns, wherein the nextresult set comprises the plurality of child illumination patterns. 14.The system of claim 13, wherein generating the plurality of childillumination patterns comprises generating a first child illuminationpattern by randomly modifying one of the portion of the plurality ofillumination patterns.
 15. The system of claim 13, wherein generatingthe plurality of child illumination patterns comprises applying atwo-point cross-over operator to at least one of the portion of.
 16. Thesystem of claim 13, wherein generating the plurality of childillumination patterns comprises passing at least one of the portion ofthe plurality of illumination patterns to the plurality of childillumination patterns.
 17. The system of claim 13, wherein generating anext result set comprising a second plurality of illumination patternscomprises: randomly assigning a velocity to each of the plurality ofillumination patterns; for each of the plurality of illuminationpatterns, advancing the illumination pattern one time unit based on therandom velocity of the illumination pattern.
 18. The system of claim 13,wherein generating a next result set comprising a second plurality ofillumination patterns further comprises: updating the velocity of atleast one of the plurality of illumination patterns considering at leastone of a best individual position of the at least one illuminationpattern, a globally best position, a cognitive constant and a socialconstant.
 19. An imaging method for generating an image of a sub-surfacefeature of an object through a surface of the object, the methodcomprising: calculating an optimized illumination pattern of a pluralityof illumination sources for imaging the sub-surface feature, wherein theplurality of illumination sources are positioned around the sub-surfacefeature of the object, wherein each of the illumination sources directsillumination in an imaging wavelength range towards the surface at anangle relative to the normal of the surface for imaging the sub-surfacefeature; and activating the optimized illumination pattern; andinstructing an imaging device to capture an image of the sub-surfacefeature with the imaging device based on reflected illumination from theoptimized illumination pattern.